NEWS.md

@@ -8,7 +8,6 @@ This project does its best to adhere to semantic versioning.
   - updated tests
 - updated maintainer
 - updated vignettes
-- use precomputed data to create vignettes
 - new features: 
   - added param bbox for osem_boxes function
   - support of multiple grouptags

R/measurement.R

@@ -72,7 +72,7 @@ osem_measurements.default = function (x, ...) {
 #' @describeIn osem_measurements Get measurements by a spatial filter.
 #' @export
 #' @examples
-#' \dontrun{
+#' \donttest{
 #'   # get measurements from sensors within a custom WGS84 bounding box
 #'   bbox = structure(c(7, 51, 8, 52), class = 'bbox')
 #'   m = osem_measurements(bbox, 'Temperatur')

man/osem_measurements.Rd

@@ -107,7 +107,7 @@ a bounding box spanning the whole world.
     'height'
   ))
 }
-\dontrun{
+\donttest{
   # get measurements from sensors within a custom WGS84 bounding box
   bbox = structure(c(7, 51, 8, 52), class = 'bbox')
   m = osem_measurements(bbox, 'Temperatur')

vignettes/osem-history.Rmd

@@ -43,10 +43,7 @@ So the first step is to retrieve *all the boxes*:
 ```{r download}
 # if you want to see results for a specific subset of boxes,
 # just specify a filter such as grouptag='ifgi' here
-
+boxes = osem_boxes(cache = '.')
-# boxes = osem_boxes(cache = '.')
-boxes = readRDS('boxes_precomputed.rds')  # read precomputed file to save resources 
-
 ```
 
 # Plot count of boxes by time {.tabset}

vignettes/osem-history_revised.Rmd

@@ -45,9 +45,8 @@ So the first step is to retrieve *all the boxes*.
 ```{r download, results='hide', message=FALSE, warning=FALSE}
 # if you want to see results for a specific subset of boxes,
 # just specify a filter such as grouptag='ifgi' here
-
+boxes_all = osem_boxes(cache = '.')
-# boxes = osem_boxes(cache = '.')
+boxes = boxes_all
-boxes = readRDS('boxes_precomputed.rds')  # read precomputed file to save resources 
 ```
 # Introduction
 In the following we just want to have a look at the boxes created in 2022, so we filter for them. 
@@ -66,7 +65,7 @@ summary(boxes) -> summary.data.frame
 Another feature of interest is the spatial distribution of the boxes: `plot()`
 can help us out here. This function requires a bunch of optional dependencies though.
 
-```{r, message=F, warning=F}
+```{r message=F, warning=F}
 if (!require('maps'))     install.packages('maps')
 if (!require('maptools')) install.packages('maptools')
 if (!require('rgeos'))    install.packages('rgeos')

vignettes/osem-intro.Rmd

@@ -32,8 +32,7 @@ datasets' structure.
 library(magrittr)
 library(opensensmapr)
 
-# all_sensors = osem_boxes(cache = '.')
+all_sensors = osem_boxes(cache = '.')
-all_sensors = readRDS('boxes_precomputed.rds')  # read precomputed file to save resources 
 ```
 ```{r}
 summary(all_sensors)
@@ -48,7 +47,7 @@ couple of minutes ago.
 Another feature of interest is the spatial distribution of the boxes: `plot()`
 can help us out here. This function requires a bunch of optional dependencies though.
 
-```{r, message=F, warning=F}
+```{r message=F, warning=F}
 if (!require('maps'))     install.packages('maps')
 if (!require('maptools')) install.packages('maptools')
 if (!require('rgeos'))    install.packages('rgeos')
@@ -82,7 +81,7 @@ We should check how many sensor stations provide useful data: We want only those
 boxes with a PM2.5 sensor, that are placed outdoors and are currently submitting
 measurements:
 
-```{r results = F, eval=FALSE}
+```{r results = F}
 pm25_sensors = osem_boxes(
   exposure = 'outdoor',
   date = Sys.time(), # ±4 hours
@@ -90,8 +89,6 @@ pm25_sensors = osem_boxes(
 )
 ```
 ```{r}
-pm25_sensors = readRDS('pm25_sensors.rds') # read precomputed file to save resources 
-
 summary(pm25_sensors)
 plot(pm25_sensors)
 ```
@@ -104,16 +101,12 @@ We could call `osem_measurements(pm25_sensors)` now, however we are focusing on
 a restricted area of interest, the city of Berlin.
 Luckily we can get the measurements filtered by a bounding box:
 
-```{r, results=F, message=F}
+```{r. eval = FALSE}
 library(sf)
 library(units)
 library(lubridate)
 library(dplyr)
 
-```
-
-Since the API takes quite long to response measurements, especially filtered on space and time, we do not run the following chunks for publication of the package on CRAN.
-```{r bbox, results = F, eval=FALSE}
 # construct a bounding box: 12 kilometers around Berlin
 berlin = st_point(c(13.4034, 52.5120)) %>%
   st_sfc(crs = 4326) %>%
@@ -121,6 +114,10 @@ berlin = st_point(c(13.4034, 52.5120)) %>%
   st_buffer(set_units(12, km)) %>%
   st_transform(4326) %>% # the opensensemap expects WGS 84
   st_bbox()
+```
+
+Since the API takes quite long to response measurements, especially filtered on space and time, we do not run the following chunks for publication of the package on CRAN.
+```{r results = F, eval=FALSE}
 pm25 = osem_measurements(
   berlin,
   phenomenon = 'PM2.5',
@@ -128,17 +125,13 @@ pm25 = osem_measurements(
   to = now()
 )
 
-```
-
-```{r}
-pm25 = readRDS('pm25_berlin.rds') # read precomputed file to save resources 
 plot(pm25)
 ```
 
 Now we can get started with actual spatiotemporal data analysis.
 First, lets mask the seemingly uncalibrated sensors:
 
-```{r, warning=F}
+```{r, eval=FALSE}
 outliers = filter(pm25, value > 100)$sensorId
 bad_sensors = outliers[, drop = T] %>% levels()
 
@@ -147,13 +140,13 @@ pm25 = mutate(pm25, invalid = sensorId %in% bad_sensors)
 
 Then plot the measuring locations, flagging the outliers:
 
-```{r}
+```{r, eval=FALSE}
 st_as_sf(pm25) %>% st_geometry() %>% plot(col = factor(pm25$invalid), axes = T)
 ```
 
 Removing these sensors yields a nicer time series plot:
 
-```{r}
+```{r, eval=FALSE}
 pm25 %>% filter(invalid == FALSE) %>% plot()
 ```